





"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'fnet'





      def __init__(			self ,		lowercase=32_000 ,		lowercase=768 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu_new" ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=4 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=512 ,		lowercase=3 ,		lowercase=1 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			use_tpu_fourier_optimizations
            lowerCAmelCase    		=			tpu_short_seq_length






